{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38d0ddcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "import torch\n",
    "torch.cuda.device_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b6abf5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(model='encnet', backbone='clip_vitl16_384', dataset='ade20k', workers=16, base_size=520, crop_size=480, train_split='train', aux=False, se_loss=False, se_weight=0.2, batch_size=16, test_batch_size=16, no_cuda=False, seed=1, weights='', eval=False, export=None, acc_bn=False, test_val=False, no_val=False, module='lseg', data_path='../datasets/', scale_inv=True, widehead=False, widehead_hr=False, ignore_index=-1, label_src='default', arch_option=0, block_depth=0, activation='lrelu', cuda=True)\n",
      "** Use norm [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] as the mean and std **\n",
      "{'base_size': 520, 'crop_size': 480}\n",
      "train\n",
      "BaseDataset: base_size 520, crop_size 480\n",
      "len(img_paths): 20210\n",
      "val\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rranftl/anaconda3/envs/lseg_releases/lib/python3.9/site-packages/deprecate/deprecation.py:115: LightningDeprecationWarning: The `Accuracy` was deprecated since v1.3.0 in favor of `torchmetrics.classification.accuracy.Accuracy`. It will be removed in v1.5.0.\n",
      "  stream(template_mgs % msg_args)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import argparse\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from collections import OrderedDict\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.utils import data\n",
    "import torchvision.transforms as transform\n",
    "from torch.nn.parallel.scatter_gather import gather\n",
    "\n",
    "import encoding.utils as utils\n",
    "from encoding.nn import SegmentationLosses, SyncBatchNorm\n",
    "from encoding.parallel import DataParallelModel, DataParallelCriterion\n",
    "from encoding.datasets import test_batchify_fn \n",
    "from encoding.models.sseg import BaseNet\n",
    "from additional_utils.models import LSeg_MultiEvalModule\n",
    "from modules.lseg_module import LSegModule\n",
    "\n",
    "import math\n",
    "import types\n",
    "import functools\n",
    "import torchvision.transforms as torch_transforms\n",
    "import copy\n",
    "import itertools\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import clip\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as mplc\n",
    "import matplotlib.figure as mplfigure\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.backends.backend_agg import FigureCanvasAgg\n",
    "from data import get_dataset\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "class Options:\n",
    "    def __init__(self):\n",
    "        parser = argparse.ArgumentParser(description=\"PyTorch Segmentation\")\n",
    "        # model and dataset\n",
    "        parser.add_argument(\n",
    "            \"--model\", type=str, default=\"encnet\", help=\"model name (default: encnet)\"\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--backbone\",\n",
    "            type=str,\n",
    "            default=\"clip_vitl16_384\",\n",
    "            help=\"backbone name (default: resnet50)\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--dataset\",\n",
    "            type=str,\n",
    "            default=\"ade20k\",\n",
    "            help=\"dataset name (default: pascal12)\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--workers\", type=int, default=16, metavar=\"N\", help=\"dataloader threads\"\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--base-size\", type=int, default=520, help=\"base image size\"\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--crop-size\", type=int, default=480, help=\"crop image size\"\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--train-split\",\n",
    "            type=str,\n",
    "            default=\"train\",\n",
    "            help=\"dataset train split (default: train)\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--aux\", action=\"store_true\", default=False, help=\"Auxilary Loss\"\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--se-loss\",\n",
    "            action=\"store_true\",\n",
    "            default=False,\n",
    "            help=\"Semantic Encoding Loss SE-loss\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--se-weight\", type=float, default=0.2, help=\"SE-loss weight (default: 0.2)\"\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--batch-size\",\n",
    "            type=int,\n",
    "            default=16,\n",
    "            metavar=\"N\",\n",
    "            help=\"input batch size for \\\n",
    "                            training (default: auto)\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--test-batch-size\",\n",
    "            type=int,\n",
    "            default=16,\n",
    "            metavar=\"N\",\n",
    "            help=\"input batch size for \\\n",
    "                            testing (default: same as batch size)\",\n",
    "        )\n",
    "        # cuda, seed and logging\n",
    "        parser.add_argument(\n",
    "            \"--no-cuda\",\n",
    "            action=\"store_true\",\n",
    "            default=False,\n",
    "            help=\"disables CUDA training\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--seed\", type=int, default=1, metavar=\"S\", help=\"random seed (default: 1)\"\n",
    "        )\n",
    "        # checking point\n",
    "        parser.add_argument(\n",
    "            \"--weights\", type=str, default='', help=\"checkpoint to test\"\n",
    "        )\n",
    "        # evaluation option\n",
    "        parser.add_argument(\n",
    "            \"--eval\", action=\"store_true\", default=False, help=\"evaluating mIoU\"\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--export\",\n",
    "            type=str,\n",
    "            default=None,\n",
    "            help=\"put the path to resuming file if needed\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--acc-bn\",\n",
    "            action=\"store_true\",\n",
    "            default=False,\n",
    "            help=\"Re-accumulate BN statistics\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--test-val\",\n",
    "            action=\"store_true\",\n",
    "            default=False,\n",
    "            help=\"generate masks on val set\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--no-val\",\n",
    "            action=\"store_true\",\n",
    "            default=False,\n",
    "            help=\"skip validation during training\",\n",
    "        )\n",
    "\n",
    "        parser.add_argument(\n",
    "            \"--module\",\n",
    "            default='lseg',\n",
    "            help=\"select model definition\",\n",
    "        )\n",
    "\n",
    "        # test option\n",
    "        parser.add_argument(\n",
    "            \"--data-path\", type=str, default='../datasets/', help=\"path to test image folder\"\n",
    "        )\n",
    "\n",
    "        parser.add_argument(\n",
    "            \"--no-scaleinv\",\n",
    "            dest=\"scale_inv\",\n",
    "            default=True,\n",
    "            action=\"store_false\",\n",
    "            help=\"turn off scaleinv layers\",\n",
    "        )\n",
    "\n",
    "        parser.add_argument(\n",
    "            \"--widehead\", default=False, action=\"store_true\", help=\"wider output head\"\n",
    "        )\n",
    "\n",
    "        parser.add_argument(\n",
    "            \"--widehead_hr\",\n",
    "            default=False,\n",
    "            action=\"store_true\",\n",
    "            help=\"wider output head\",\n",
    "        )\n",
    "        parser.add_argument(\n",
    "            \"--ignore_index\",\n",
    "            type=int,\n",
    "            default=-1,\n",
    "            help=\"numeric value of ignore label in gt\",\n",
    "        )\n",
    "        \n",
    "        parser.add_argument(\n",
    "            \"--label_src\",\n",
    "            type=str,\n",
    "            default=\"default\",\n",
    "            help=\"how to get the labels\",\n",
    "        )\n",
    "        \n",
    "        parser.add_argument(\n",
    "            \"--arch_option\",\n",
    "            type=int,\n",
    "            default=0,\n",
    "            help=\"which kind of architecture to be used\",\n",
    "        )\n",
    "\n",
    "        parser.add_argument(\n",
    "            \"--block_depth\",\n",
    "            type=int,\n",
    "            default=0,\n",
    "            help=\"how many blocks should be used\",\n",
    "        )\n",
    "\n",
    "        parser.add_argument(\n",
    "            \"--activation\",\n",
    "            choices=['lrelu', 'tanh'],\n",
    "            default=\"lrelu\",\n",
    "            help=\"use which activation to activate the block\",\n",
    "        )\n",
    "\n",
    "        self.parser = parser\n",
    "\n",
    "    def parse(self):\n",
    "        args = self.parser.parse_args(args=[]) \n",
    "        args.cuda = not args.no_cuda and torch.cuda.is_available()\n",
    "        print(args)\n",
    "        return args\n",
    "    \n",
    "\n",
    "def get_new_pallete(num_cls):\n",
    "    n = num_cls\n",
    "    pallete = [0]*(n*3)\n",
    "    for j in range(0,n):\n",
    "            lab = j\n",
    "            pallete[j*3+0] = 0\n",
    "            pallete[j*3+1] = 0\n",
    "            pallete[j*3+2] = 0\n",
    "            i = 0\n",
    "            while (lab > 0):\n",
    "                    pallete[j*3+0] |= (((lab >> 0) & 1) << (7-i))\n",
    "                    pallete[j*3+1] |= (((lab >> 1) & 1) << (7-i))\n",
    "                    pallete[j*3+2] |= (((lab >> 2) & 1) << (7-i))\n",
    "                    i = i + 1\n",
    "                    lab >>= 3\n",
    "    return pallete\n",
    "\n",
    "def get_new_mask_pallete(npimg, new_palette, out_label_flag=False, labels=None):\n",
    "    \"\"\"Get image color pallete for visualizing masks\"\"\"\n",
    "    # put colormap\n",
    "    out_img = Image.fromarray(npimg.squeeze().astype('uint8'))\n",
    "    out_img.putpalette(new_palette)\n",
    "\n",
    "    if out_label_flag:\n",
    "        assert labels is not None\n",
    "        u_index = np.unique(npimg)\n",
    "        patches = []\n",
    "        for i, index in enumerate(u_index):\n",
    "            label = labels[index]\n",
    "            cur_color = [new_palette[index * 3] / 255.0, new_palette[index * 3 + 1] / 255.0, new_palette[index * 3 + 2] / 255.0]\n",
    "            red_patch = mpatches.Patch(color=cur_color, label=label)\n",
    "            patches.append(red_patch)\n",
    "    return out_img, patches\n",
    "\n",
    "args = Options().parse()\n",
    "\n",
    "torch.manual_seed(args.seed)\n",
    "args.test_batch_size = 1 \n",
    "alpha=0.5\n",
    "    \n",
    "args.scale_inv = False\n",
    "args.widehead = True\n",
    "args.dataset = 'ade20k'\n",
    "args.backbone = 'clip_vitl16_384'\n",
    "args.weights = 'checkpoints/demo_e200.ckpt'\n",
    "args.ignore_index = 255\n",
    "\n",
    "module = LSegModule.load_from_checkpoint(\n",
    "    checkpoint_path=args.weights,\n",
    "    data_path=args.data_path,\n",
    "    dataset=args.dataset,\n",
    "    backbone=args.backbone,\n",
    "    aux=args.aux,\n",
    "    num_features=256,\n",
    "    aux_weight=0,\n",
    "    se_loss=False,\n",
    "    se_weight=0,\n",
    "    base_lr=0,\n",
    "    batch_size=1,\n",
    "    max_epochs=0,\n",
    "    ignore_index=args.ignore_index,\n",
    "    dropout=0.0,\n",
    "    scale_inv=args.scale_inv,\n",
    "    augment=False,\n",
    "    no_batchnorm=False,\n",
    "    widehead=args.widehead,\n",
    "    widehead_hr=args.widehead_hr,\n",
    "    map_locatin=\"cpu\",\n",
    "    arch_option=0,\n",
    "    block_depth=0,\n",
    "    activation='lrelu',\n",
    ")\n",
    "\n",
    "input_transform = module.val_transform\n",
    "\n",
    "# dataloader\n",
    "loader_kwargs = (\n",
    "    {\"num_workers\": args.workers, \"pin_memory\": True} if args.cuda else {}\n",
    ")\n",
    "\n",
    "# model\n",
    "if isinstance(module.net, BaseNet):\n",
    "    model = module.net\n",
    "else:\n",
    "    model = module\n",
    "    \n",
    "model = model.eval()\n",
    "model = model.cpu()\n",
    "scales = (\n",
    "    [0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25]\n",
    "    if args.dataset == \"citys\"\n",
    "    else [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]\n",
    ")  \n",
    "\n",
    "model.mean = [0.5, 0.5, 0.5]\n",
    "model.std = [0.5, 0.5, 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9c0e0bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiEvalModule: base_size 520, crop_size 480\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LSeg_MultiEvalModule(\n",
       "  (module): LSegModule(\n",
       "    (train_accuracy): Accuracy()\n",
       "    (val_accuracy): Accuracy()\n",
       "    (net): LSegNet(\n",
       "      (clip_pretrained): CLIP(\n",
       "        (visual): VisionTransformer(\n",
       "          (conv1): Conv2d(3, 768, kernel_size=(32, 32), stride=(32, 32), bias=False)\n",
       "          (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (transformer): Transformer(\n",
       "            (resblocks): Sequential(\n",
       "              (0): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (1): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (2): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (3): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (4): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (5): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (6): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (7): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (8): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (9): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (10): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "              (11): ResidualAttentionBlock(\n",
       "                (attn): MultiheadAttention(\n",
       "                  (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "                (mlp): Sequential(\n",
       "                  (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                  (gelu): QuickGELU()\n",
       "                  (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                )\n",
       "                (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "          (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (transformer): Transformer(\n",
       "          (resblocks): Sequential(\n",
       "            (0): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (1): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (2): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (3): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (4): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (5): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (6): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (7): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (8): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (9): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (10): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "            (11): ResidualAttentionBlock(\n",
       "              (attn): MultiheadAttention(\n",
       "                (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Sequential(\n",
       "                (c_fc): Linear(in_features=512, out_features=2048, bias=True)\n",
       "                (gelu): QuickGELU()\n",
       "                (c_proj): Linear(in_features=2048, out_features=512, bias=True)\n",
       "              )\n",
       "              (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (token_embedding): Embedding(49408, 512)\n",
       "        (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "      (pretrained): Module(\n",
       "        (model): VisionTransformer(\n",
       "          (patch_embed): PatchEmbed(\n",
       "            (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16))\n",
       "            (norm): Identity()\n",
       "          )\n",
       "          (pos_drop): Dropout(p=0.0, inplace=False)\n",
       "          (blocks): Sequential(\n",
       "            (0): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (1): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (2): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (3): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (4): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (5): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (6): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (7): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (8): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (9): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (10): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (11): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (12): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (13): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (14): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (15): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (16): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (17): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (18): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (19): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (20): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (21): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (22): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (23): Block(\n",
       "              (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (attn): Attention(\n",
       "                (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
       "                (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "                (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "              (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "                (act): GELU()\n",
       "                (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "                (drop): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "          (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
       "          (pre_logits): Identity()\n",
       "          (head): Linear(in_features=1024, out_features=1000, bias=True)\n",
       "        )\n",
       "        (act_postprocess1): Sequential(\n",
       "          (0): ProjectReadout(\n",
       "            (project): Sequential(\n",
       "              (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "              (1): GELU()\n",
       "            )\n",
       "          )\n",
       "          (1): Transpose()\n",
       "          (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
       "          (3): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (4): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(4, 4))\n",
       "        )\n",
       "        (act_postprocess2): Sequential(\n",
       "          (0): ProjectReadout(\n",
       "            (project): Sequential(\n",
       "              (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "              (1): GELU()\n",
       "            )\n",
       "          )\n",
       "          (1): Transpose()\n",
       "          (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
       "          (3): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (4): ConvTranspose2d(512, 512, kernel_size=(2, 2), stride=(2, 2))\n",
       "        )\n",
       "        (act_postprocess3): Sequential(\n",
       "          (0): ProjectReadout(\n",
       "            (project): Sequential(\n",
       "              (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "              (1): GELU()\n",
       "            )\n",
       "          )\n",
       "          (1): Transpose()\n",
       "          (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
       "          (3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (act_postprocess4): Sequential(\n",
       "          (0): ProjectReadout(\n",
       "            (project): Sequential(\n",
       "              (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "              (1): GELU()\n",
       "            )\n",
       "          )\n",
       "          (1): Transpose()\n",
       "          (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
       "          (3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (4): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "        )\n",
       "      )\n",
       "      (scratch): Module(\n",
       "        (layer1_rn): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (layer2_rn): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (layer3_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (layer4_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (refinenet1): FeatureFusionBlock_custom(\n",
       "          (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (resConfUnit1): ResidualConvUnit_custom(\n",
       "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (activation): ReLU()\n",
       "            (skip_add): FloatFunctional(\n",
       "              (activation_post_process): Identity()\n",
       "            )\n",
       "          )\n",
       "          (resConfUnit2): ResidualConvUnit_custom(\n",
       "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (activation): ReLU()\n",
       "            (skip_add): FloatFunctional(\n",
       "              (activation_post_process): Identity()\n",
       "            )\n",
       "          )\n",
       "          (skip_add): FloatFunctional(\n",
       "            (activation_post_process): Identity()\n",
       "          )\n",
       "        )\n",
       "        (refinenet2): FeatureFusionBlock_custom(\n",
       "          (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (resConfUnit1): ResidualConvUnit_custom(\n",
       "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (activation): ReLU()\n",
       "            (skip_add): FloatFunctional(\n",
       "              (activation_post_process): Identity()\n",
       "            )\n",
       "          )\n",
       "          (resConfUnit2): ResidualConvUnit_custom(\n",
       "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (activation): ReLU()\n",
       "            (skip_add): FloatFunctional(\n",
       "              (activation_post_process): Identity()\n",
       "            )\n",
       "          )\n",
       "          (skip_add): FloatFunctional(\n",
       "            (activation_post_process): Identity()\n",
       "          )\n",
       "        )\n",
       "        (refinenet3): FeatureFusionBlock_custom(\n",
       "          (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (resConfUnit1): ResidualConvUnit_custom(\n",
       "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (activation): ReLU()\n",
       "            (skip_add): FloatFunctional(\n",
       "              (activation_post_process): Identity()\n",
       "            )\n",
       "          )\n",
       "          (resConfUnit2): ResidualConvUnit_custom(\n",
       "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (activation): ReLU()\n",
       "            (skip_add): FloatFunctional(\n",
       "              (activation_post_process): Identity()\n",
       "            )\n",
       "          )\n",
       "          (skip_add): FloatFunctional(\n",
       "            (activation_post_process): Identity()\n",
       "          )\n",
       "        )\n",
       "        (refinenet4): FeatureFusionBlock_custom(\n",
       "          (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (resConfUnit1): ResidualConvUnit_custom(\n",
       "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (activation): ReLU()\n",
       "            (skip_add): FloatFunctional(\n",
       "              (activation_post_process): Identity()\n",
       "            )\n",
       "          )\n",
       "          (resConfUnit2): ResidualConvUnit_custom(\n",
       "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (activation): ReLU()\n",
       "            (skip_add): FloatFunctional(\n",
       "              (activation_post_process): Identity()\n",
       "            )\n",
       "          )\n",
       "          (skip_add): FloatFunctional(\n",
       "            (activation_post_process): Identity()\n",
       "          )\n",
       "        )\n",
       "        (head1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (output_conv): Sequential(\n",
       "          (0): Interpolate()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (criterion): SegmentationLosses(\n",
       "      (bceloss): BCELoss()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator = LSeg_MultiEvalModule(\n",
    "    model, scales=scales, flip=True\n",
    ").cuda()\n",
    "evaluator.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc383c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9dec97e340>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = 'inputs/cat1.jpeg'\n",
    "#img_path = 'inputs/catdog.png'\n",
    "crop_size = 480\n",
    "padding = [0.0] * 3\n",
    "image = Image.open(img_path)\n",
    "image = np.array(image)\n",
    "transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),\n",
    "    ]\n",
    ")\n",
    "image = transform(image).unsqueeze(0)\n",
    "img = image[0].permute(1,2,0)\n",
    "img = img * 0.5 + 0.5\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd8e70b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "args.label_src = 'plant,grass,cat,stone,other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6682f086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Input label value: plant,grass,cat,stone,other **\n",
      "** MultiEvalModule parallel_forward phase: ['plant', 'grass', 'cat', 'stone', 'other'] **\n",
      "** MultiEvalModule forward phase: ['plant', 'grass', 'cat', 'stone', 'other'] **\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rranftl/anaconda3/envs/lseg_releases/lib/python3.9/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "labels = []\n",
    "print('** Input label value: {} **'.format(args.label_src))\n",
    "lines = args.label_src.split(',')\n",
    "for line in lines:\n",
    "    label = line\n",
    "    labels.append(label)\n",
    "\n",
    "with torch.no_grad():\n",
    "    outputs = evaluator.parallel_forward(image, labels) #evaluator.forward(image, labels) #parallel_forward\n",
    "    #outputs = model(image,labels)\n",
    "    predicts = [\n",
    "        torch.max(output, 1)[1].cpu().numpy() \n",
    "        for output in outputs\n",
    "    ]\n",
    "    \n",
    "predict = predicts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85e8cc6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9e80612a90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show results\n",
    "new_palette = get_new_pallete(len(labels))\n",
    "mask, patches = get_new_mask_pallete(predict, new_palette, out_label_flag=True, labels=labels)\n",
    "img = image[0].permute(1,2,0)\n",
    "img = img * 0.5 + 0.5\n",
    "img = Image.fromarray(np.uint8(255*img)).convert(\"RGBA\")\n",
    "seg = mask.convert(\"RGBA\")\n",
    "out = Image.blend(img, seg, alpha)\n",
    "plt.axis('off')\n",
    "plt.imshow(img)\n",
    "plt.figure()\n",
    "plt.legend(handles=patches, loc='upper right', bbox_to_anchor=(1.5, 1), prop={'size': 20})\n",
    "plt.axis('off')\n",
    "plt.imshow(seg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
